#--- simulation ---
set.seed(1)
n0 <- 100; n1 <- 10000; n <- n0 + n1; p <- 500
X <- matrix(rnorm(n=n*p),nrow=n,ncol=p)
beta <- rnorm(p)
prior <- beta + rnorm(p)
y <- X %*% beta
#--- train-test split ---
foldid <- rep(c(0,1),times=c(n0,n1))
y0 <- y[foldid==0]
X0 <- X[foldid==0,]
y1 <- y[foldid==1]
X1 <- X[foldid==1,]
object <- transreg(y=y0,X=X0,prior=prior)
#--- fitted values ---
y0_hat <- fitted(object)
mean((y0-y0_hat)^2)
#--- predicted values ---
y1_hat <- predict(object,newx=X1)
mean((y1-y1_hat)^2) # increase in MSE?
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